This study investigates the hot deformation behavior of an API X70 microalloyed steel using a PROCESSING MAP approach. Cylindrical specimens were subjected to compression tests at temperatures ranging from 950 to 1100 °C and strain rates from 0.001 to 1 s⁻¹. Flow curves revealed typical features of work hardening, dynamic recovery, and dynamic recrystallization. Power dissipation efficiency and instability criteria were calculated using a dynamic material model to construct PROCESSING MAPs at various strain levels. The MAPs identified three optimal hot deformation regions at higher strains, with the most favorable zone (efficiency of 25–33%) located at 1075–1100 °C and 0.001–0.005 s⁻¹. Microstructural validation confirmed these safe zones, while unstable regions were characterized by crack formation and deformation bands. A constitutive analysis yielded an apparent activation energy for hot deformation of 572.5 kJ/mol. The findings provide valuable guidance for optimizing hot working conditions of API X70 steel.

PROCESSING MAP for hot working of Ti-IF steel has been developed in the temperature range of 750 to 1100 °C and strain rate of 0.01 to 100 s-1. This MAP in the austenite region exhibits a single domain with a peak efficiency of 45% occurring at 1025 °C and strain rate of 0.02 s-1. The domain extends over the temperature range of 1000 to 1100 °C and strain rate range of 0.01 to 1 s-1. The true stress-strain curves and microstructural observations show the occurrence of dynamic recrystallization in this domain. In two phase regions, where austenite and ferrite are present together, flow localization occurs in the form of bands with a fine grained structure as a result of dynamic recrystallization in the bands. These deformation bands are formed at 45° with respect to axial direction of compression. The PROCESSING MAP in ferrite region exhibits a domain with a peak efficiency of 38% occurring at 825 °C and strain rate of 0.02 s-1, so this domain extends over the temperature range of 800 to 850 °C and strain rate range of 0.01 to 0.5 s-1. The true stress-strain curves and microstructural results confirm the occurrence of partially dynamic recrystallization in this domain.

Anxiety disorders are one of the most common and debilitating mental disorders worldwide. On the other hand, since 2019, with the outbreak of Covid-19, anxiety has increased among people, especially the medical staff. Currently, anxiety is diagnosed (when the symptoms are severe enough) using a questionnaire by a specialist. To resolve this shortcoming, researchers have recently paid attention to the use of brain signals. Consequently, the present study aimed to diagnose anxiety using brain signals. The novelty of this study is the use of the Chebyshev chaotic MAP for the first time in biological signal analysis. It used the DASPS database, which includes a 14-channel electroencephalogram (EEG) of 23 people (10 men and 13 women, with a mean age of 30 years). The self-assessment manikin scores were used to divide anxiety into two and four levels. First, the data were normalized. Then, the chaotic MAP was reconstructed and divided into 128 strips. The density of points in each of the strips was calculated. Two indicators were considered as features, (1) maximum density and (2) its corresponding sample. Finally, features were applied to Support Vector Machines (SVM) and k-Nearest Neighbors (K-NN) in 5 ways, (1) feature 1 of all channels, (2) feature1 MAPping of all channels using principal component analysis (PCA), (3) feature 2 of all channels, (4) feature 2 MAPping of all channels using PCA and (5) each feature - each channel separately. The results show a maximum accuracy of 93.75% for diagnosing two levels of anxiety and 96.15% for diagnosing four levels of anxiety. In addition, K-NN outperformed SVM. Accordingly, the proposed algorithm can be introduced as a suitable approach for diagnosing anxiety.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image PROCESSING technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

In this study, the hot deformation PROCESSING MAP of AISI 304 austenitic stainless steel in two initial grain sizes of 15 and 40 μ m was investigated. For this purpose, cylindrical samples were used in the hot compression test at the temperature ranges of 950-1100 ° C and the strain rates of 0. 005-0. 5% s-1. At first, the relationship between the peak stress and Zener-Hollomon parameter was obtained and their microstructures were studied, then the strain rate sensitivity and the PROCESSING MAP were determined at the strains of 0. 5 and 0. 7. It was found that in the range of temperature and strain rate studied here, the prevailing softening mechanism is the dynamic recrystallization process. Instability regions were observed at lower temperatures and higher strain rates, associated with the occurrence of necklace phenomena in both grain sizes especially the emphasized semi-necklace structure in the coarse-grained steel, and moreover to the formation of geometrically necessary dislocation along the grain boundaries as well as the development of cell structure interior the grains in the fine grain steel. Results also showed that with increasing temperature and decreasing strain rate, the power dissipation efficiency and the strain rate sensitivity are both increased, indicating an increase in the volume of recrystallized material in a constant strain and a decrease in the susceptibility to the localization of the plastic flow.

Hot workability of as-extruded high Gd content Mg-5Gd-0, 5Zn-0. 5Zr-2. 5Nd alloy was investigated using the hot compression test in a temperature range of 300-500  C and strain rates of 0. 001-1s-1. Hot workability assessment was conducted by capturing microstructural evolution of high temperature deformed samples, and by constructing power dissipation and instability MAPs. Using experimental data of hot compression tests, the power dissipation MAP of the alloy was constructed, in which a domain of dynamic recrystallization (DRX) occurred at the temperature range of 350-450  C and strain rate of 0. 001-0. 1 s-1, representing the optimum hot working window. Furthermore, the PROCESSING MAP of the alloy was constructed, and flow instability regions were also indicated based on the Ziegler's flow instability criterion.

In traditional speech PROCESSING, feature extraction and classification were conducted as separate steps. The advent of deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and deep architectures across diverse speech PROCESSING applications. This paper introduces different types of parametric filters and discusses their integration into various deep architectures. Additionally, it examines the specific applications in speech PROCESSING where these filters have proven effective.

Introduction and Objectives: Determining the constitutive equations which describe the material flow stress, microstructural investigation, and preparing a PROCESSING MAP are essential for designing and optimizing metals forming. The aim of this paper is to obtain a relationship between stress, strain, temperature, and strain rate for Al-7. 5Mg alloy in order to use it in the forming process. Materials and Methods: In this study, stress-strain diagrams at four temperatures and three strain rates and friction elimination using experimental and computational methods were used. The activation energy value of hot deformation was calculated in the Arrhenius model. A PROCESSING MAP at strain 0. 6 was drawn to establish the relationship between stress, temperature, and strain rate, and the instability region at a specific temperature and strain rate was determined. The metallography examination was performed to study the microstructure of the alloy. Results: In the Arrhenius model, the activation energy value of hot deformation increased with increasing strain. As the temperature decreased and the strain rate increased, the Zener-Hollomon parameter also increased. The instability region increased with increasing temperature and decreasing strain rate. The metallography test results showed that dynamic recrystallization occurred due to the high magnesium content in this alloy. Conclusion: In this paper, the Arrhenius model (as a phenomenological model) was correctly extracted using a hot plane strain test on an extruded plate of Al-7. 5Mg alloy, and a relationship between stress, temperature, strain, and strain rate was obtained using the constitutive equations and the Arrhenius model.